Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session Q52: Advanced Technologies for Medical Physics
3:00 PM–5:36 PM,
Wednesday, March 8, 2023
Room: Room 308
Sponsoring
Units:
GMED GDS
Chair: Wojciech Zbijewski, Johns Hopkins University; Alejandro Sisniega
Abstract: Q52.00005 : Using natural language processing to extract features from clinical notes for medical physics quality assurance
4:12 PM–4:24 PM
Presenter:
Connor Thropp
(Brown University)
Authors:
Connor Thropp
(Brown University)
Laura Buchanan
(Lifespan Cancer Institute; Warren Alpert Medical School of Brown University)
Timothy Leech
(Lifespan Cancer Institute)
Qiongge Li
(Lifespan Cancer Institute; Warren Alpert Medical School of Brown University)
Eric Klein
(Lifespan Cancer Institute; Warren Alpert Medical School of Brown University)
Our database contains 457 prostate cancer patients’ radiotherapy treatment records, where 83% of Gleason scores and 100% of Prostate-Specific Antigen (PSA) levels are missing. These features are critical in determining the appropriate radiotherapy dosage. We developed an algorithmic tool that utilizes basic Natural Language Processing (NLP) to extract missing features from physicians’ notes. The methods included tokenization, filtering stop word, chunking, lemmatizing, and tagging to capture the missing data.
Our prototype analysis consisted of using NLP to query 100 prostate patients’ clinical notes to find missing PSA and Gleason scores. Manual validation was performed. The results show that 64% of missing PSA values and 37% of missing Gleason scores were successfully restored. The sensitivity and specificity for finding PSA are 76% and 56% and for Gleason scores are 48% and 28%.
The restored database, with substantial data filled in by the NLP methods described, will be used to train and deploy an anomaly detection algorithm [1] that detects potentially erroneous radiotherapy prescriptions and assists with medical physics quality assurance.
[1] Li, Qiongge, et al. "A novel data-driven algorithm to predict anomalous prescription based on patient's feature set." arXiv preprint arXiv:2111.15101 (2021)
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